Sentiment analysis, also known as opinion mining, refers to the application of natural language processing, computational linguistics, and text analytics to identify and extract subjective information in source materials. Generally speaking, sentiment analysis aims to determine the attitude of a writer or a speaker, with respect to some topics or the overall tonality of a document. The attitude may be his or her judgment or evaluation, emotional state when writing or speaking, or the intended emotional communication that the author wishes to have on the reader or audience. A basic task in sentiment analysis is classifying the polarity of a given text at the document, paragraph, or sentence level—whether the expressed opinion in a document, a paragraph, or a sentence is positive, negative, or neutral. Processing of online consumer reviews is one example of the applications of sentiment analysis. Online retailers want to obtain feedback about their products from the reviews posted by various users, thereby requiring an effective method to determine users' attitudes toward their products based on a large amount of natural language documents (e.g., consumer reviews).
It is known to perform automated sentiment analysis of natural language documents by computers using data mining techniques such as latent semantic analysis, support vector machines (SVM), “bag of words” (i.e., dictionary), and semantic orientation-pointwise mutual information (SO-PMI). For example, known methods utilize “bag of words” to look for words in the documents that fall into certain sentiment categories and determine the sentiment class for each document based on the number of words found in the “bag of words”. However, since the meaning of a specific word or phrase and its associated sentiment class may change depending on its linguistic category (e.g., non, verb, adjective, etc.) or may change in conjunction with any negation/inversion elements around the word or phrase, the efficiency and accuracy of those known methods could be further improved.